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Free Discontinuity Regression: With an Application to the Economic Effects of Internet Shutdowns

Florian Gunsilius, David Van Dijcke

TL;DR

Free Discontinuity Regression is introduced, a fully nonparametric estimator that simultaneously smooths a regression surface, segments it into contiguous regions, and provably recovers the precise locations and sizes of its jumps that yields the first identification and uniform consistency results for multivariate jump surfaces.

Abstract

Sharp, multidimensional changepoints-abrupt shifts in a regression surface whose locations and magnitudes are unknown-arise in settings as varied as gene-expression profiling, financial covariance breaks, climate-regime detection, and urban socioeconomic mapping. Despite their prevalence, there are no current approaches that jointly estimate the location and size of the discontinuity set in a one-shot approach with statistical guarantees. We therefore introduce Free Discontinuity Regression (FDR), a fully nonparametric estimator that simultaneously (i) smooths a regression surface, (ii) segments it into contiguous regions, and (iii) provably recovers the precise locations and sizes of its jumps. By extending a convex relaxation of the Mumford-Shah functional to random spatial sampling and correlated noise, FDR overcomes the fixed-grid and i.i.d. noise assumptions of classical image-segmentation approaches, thus enabling its application to real-world data of any dimension. This yields the first identification and uniform consistency results for multivariate jump surfaces: under mild SBV regularity, the estimated function, its discontinuity set, and all jump sizes converge to their true population counterparts. Hyperparameters are selected automatically from the data using Stein's Unbiased Risk Estimate, and large-scale simulations up to three dimensions validate the theoretical results and demonstrate good finite-sample performance. Applying FDR to an internet shutdown in India reveals a 25-35% reduction in economic activity around the estimated shutdown boundaries-much larger than previous estimates. By unifying smoothing, segmentation, and effect-size recovery in a general statistical setting, FDR turns free-discontinuity ideas into a practical tool with formal guarantees for modern multivariate data.

Free Discontinuity Regression: With an Application to the Economic Effects of Internet Shutdowns

TL;DR

Free Discontinuity Regression is introduced, a fully nonparametric estimator that simultaneously smooths a regression surface, segments it into contiguous regions, and provably recovers the precise locations and sizes of its jumps that yields the first identification and uniform consistency results for multivariate jump surfaces.

Abstract

Sharp, multidimensional changepoints-abrupt shifts in a regression surface whose locations and magnitudes are unknown-arise in settings as varied as gene-expression profiling, financial covariance breaks, climate-regime detection, and urban socioeconomic mapping. Despite their prevalence, there are no current approaches that jointly estimate the location and size of the discontinuity set in a one-shot approach with statistical guarantees. We therefore introduce Free Discontinuity Regression (FDR), a fully nonparametric estimator that simultaneously (i) smooths a regression surface, (ii) segments it into contiguous regions, and (iii) provably recovers the precise locations and sizes of its jumps. By extending a convex relaxation of the Mumford-Shah functional to random spatial sampling and correlated noise, FDR overcomes the fixed-grid and i.i.d. noise assumptions of classical image-segmentation approaches, thus enabling its application to real-world data of any dimension. This yields the first identification and uniform consistency results for multivariate jump surfaces: under mild SBV regularity, the estimated function, its discontinuity set, and all jump sizes converge to their true population counterparts. Hyperparameters are selected automatically from the data using Stein's Unbiased Risk Estimate, and large-scale simulations up to three dimensions validate the theoretical results and demonstrate good finite-sample performance. Applying FDR to an internet shutdown in India reveals a 25-35% reduction in economic activity around the estimated shutdown boundaries-much larger than previous estimates. By unifying smoothing, segmentation, and effect-size recovery in a general statistical setting, FDR turns free-discontinuity ideas into a practical tool with formal guarantees for modern multivariate data.
Paper Structure (31 sections, 6 theorems, 125 equations, 8 figures, 2 tables, 3 algorithms)

This paper contains 31 sections, 6 theorems, 125 equations, 8 figures, 2 tables, 3 algorithms.

Key Result

Proposition 1

The optimization problem eq:main_problem admits a global solution $v^*$ in $SBV(\mathcal{X}\times \mathbb{R})\cap\{v: |Dv|\leq c\}$ for fixed $c<+\infty$ if $f\in SBV(\mathcal{X})$.

Figures (8)

  • Figure 1: Simulations: 1D to 3D
  • Figure 2: Internet Shutdown: Mobile Data Effects
  • Figure 3: Internet Shutdown: Economic Effects
  • Figure A-1: Representation of the case $\tilde{x}_k \in S_f$.
  • Figure A-2: Convex Relaxation Through Functional Lifting
  • ...and 3 more figures

Theorems & Definitions (14)

  • Definition 1
  • Proposition 1
  • Theorem 1
  • Proposition 2
  • Theorem 2
  • Corollary 1
  • Definition 2: Approximate jump points
  • Definition 3: Functions of bounded variation and SBV
  • proof
  • proof
  • ...and 4 more